Integrating demand and inventory planning to enhance service level: a case study in a peruvian craft beer SME

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This study addresses the challenges of efficiency and competitiveness in a craft beer SME in Peru by proposing a service management model based on three key tools: demand forecasting, inventory policy, and supplier management. The research arises from the need to improve logistics planning in small...

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Detalles Bibliográficos
Autores: Burga Lozada, Maria Paz, Mesia Flores, Micaela Lucia
Formato: tesis de grado
Fecha de Publicación:2025
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/23703
Enlace del recurso:https://hdl.handle.net/20.500.12724/23703
Nivel de acceso:acceso abierto
Materia:Pendiente
https://purl.org/pe-repo/ocde/ford#2.11.04
Descripción
Sumario:This study addresses the challenges of efficiency and competitiveness in a craft beer SME in Peru by proposing a service management model based on three key tools: demand forecasting, inventory policy, and supplier management. The research arises from the need to improve logistics planning in small beverage companies, where operational challenges often limit growth. The research question posed was: How can a service management model based on logistics tools improve scheduled delivery compliance in a craft brewery? The main objective was to design and implement a model that optimizes inventory and supply chain management, thereby increasing operational efficiency. Its importance lies in addressing an academic gap in the application of logistics models in SMEs within the beverage sector in emerging contexts, contributing both to scientific knowledge and socioeconomic development. Methodologically, the study employed an applied approach with a quasi-experimental design, using a craft brewery facing specific logistical difficulties as a case study. The model’s implementation resulted in a 77% compliance rate in scheduled deliveries, representing a significant improvement in service efficiency. This outcome validates the model’s usefulness and its applicability in similar contexts, recommending its adaptation to other industries facing comparable logistical challenges.
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